A frgsnn hybrid feature selection combining frgs filter and gsnn wrapper
نویسندگان
چکیده
منابع مشابه
A FRGSNN Hybrid Feature Selection Combining FRGS filter and GSNN wrapper
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no m...
متن کاملA hybrid wrapper / filter approach for feature subset selection
This work presents a hybrid wrapper/filter algorithm for feature subset selection that can use a combination of several quality criteria measures to rank the set of features of a dataset. These ranked features are used to prune the search space of subsets of possible features such that the number of times the wrapper executes the learning algorithm for a dataset with M features is reduced to O(...
متن کاملAn Effective Feature Selection Approach Using the Hybrid Filter Wrapper
Feature selection is an important data preprocessing technique and has been widely studied in data mining, machine learning and granular computing. In this paper, we introduced an effective feature selection method using the hybrid approaches, that is, use the mutual information to select the candidate feature set, then, obtain the super-reduct space from the candidate feature set by a wrapper ...
متن کاملHybrid filter-wrapper feature selection for short-term load forecasting
13 Selection of input features plays an important role in developing models for short14 term load forecasting (STLF). Previous studies along this line of research have focused 15 pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid 16 selection scheme that includes both filter and wrapper methods in constructing an 17 appropriate pool of features, coupled with the...
متن کاملDeveloping a Filter-Wrapper Feature Selection Method and its Application in Dimension Reduction of Gen Expression
Nowadays, increasing the volume of data and the number of attributes in the dataset has reduced the accuracy of the learning algorithm and the computational complexity. A dimensionality reduction method is a feature selection method, which is done through filtering and wrapping. The wrapper methods are more accurate than filter ones but perform faster and have a less computational burden. With ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Latest Trends in Engineering & Technology
سال: 2016
ISSN: 2278-621X
DOI: 10.21172/1.72.502